Cancer Causes & Control

, Volume 26, Issue 7, pp 959–972 | Cite as

Proceedings of the second international molecular pathological epidemiology (MPE) meeting

  • Shuji OginoEmail author
  • Peter T. Campbell
  • Reiko Nishihara
  • Amanda I. Phipps
  • Andrew H. Beck
  • Mark E. Sherman
  • Andrew T. Chan
  • Melissa A. Troester
  • Adam J. Bass
  • Kathryn C. Fitzgerald
  • Rafael A. Irizarry
  • Karl T. Kelsey
  • Hongmei Nan
  • Ulrike Peters
  • Elizabeth M. Poole
  • Zhi Rong Qian
  • Rulla M. Tamimi
  • Eric J. Tchetgen Tchetgen
  • Shelley S. Tworoger
  • Xuehong Zhang
  • Edward L. Giovannucci
  • Piet A. van den Brandt
  • Bernard A. Rosner
  • Molin Wang
  • Nilanjan Chatterjee
  • Colin B. Begg
Review article


Disease classification system increasingly incorporates information on pathogenic mechanisms to predict clinical outcomes and response to therapy and intervention. Technological advancements to interrogate omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics, etc.) provide widely open opportunities in population-based research. Molecular pathological epidemiology (MPE) represents integrative science of molecular pathology and epidemiology. This unified paradigm requires multidisciplinary collaboration between pathology, epidemiology, biostatistics, bioinformatics, and computational biology. Integration of these fields enables better understanding of etiologic heterogeneity, disease continuum, causal inference, and the impact of environment, diet, lifestyle, host factors (including genetics and immunity), and their interactions on disease evolution. Hence, the Second International MPE Meeting was held in Boston in December 2014, with aims to: (1) develop conceptual and practical frameworks; (2) cultivate and expand opportunities; (3) address challenges; and (4) initiate the effort of specifying guidelines for MPE. The meeting mainly consisted of presentations of method developments and recent data in various malignant neoplasms and tumors (breast, prostate, ovarian and colorectal cancers, renal cell carcinoma, lymphoma, and leukemia), followed by open discussion sessions on challenges and future plans. In particular, we recognized need for efforts to further develop statistical methodologies. This meeting provided an unprecedented opportunity for interdisciplinary collaboration, consistent with the purposes of the Big Data to Knowledge, Genetic Associations and Mechanisms in Oncology, and Precision Medicine Initiative of the US National Institute of Health. The MPE meeting series can help advance transdisciplinary population science and optimize training and education systems for twenty-first century medicine and public health.


Epidemiologic method Molecular pathologic epidemiology Personalized medicine Systems biology Translational epidemiology Unique disease principle 



Big Data to Knowledge


Body mass index


CpG island methylator phenotype


Genetic Associations and Mechanisms in Oncology


The Genetics and Epidemiology of Colorectal Cancer Consortium


Genome-wide association study


Molecular pathological epidemiology


Microsatellite instability


National Institute of Health


Strengthening the Reporting of Observational Studies in Epidemiology



We thank all of the members of the program committee, the speakers, the discussants, and the other participants of the Second International MPE Meeting on December 4–5, 2014 in Boston, MA, USA. We thank the Dana–Farber Cancer Institute (Edward J. Benz, Jr., President and CEO) for providing the meeting venue and the Department of Pathology, the Brigham and Women’s Hospital (Jeffrey A. Golden, Chairperson), and Enzymatics, Inc., for providing meals and refreshments, respectively. We also thank the Department of Pathology, the Brigham and Women’s Hospital (Jeffrey A. Golden, Chairperson); the Department of Epidemiology, the Harvard T.H. Chan School of Public Health (Michelle A. Williams, Chairperson); the Dana–Farber Harvard Cancer Center (Giovanni Parmigiani, Meir J. Stampfer, Lorelei A. Mucci, Deborah Schrag, and Charles S. Fuchs; Program Leaders); and the Channing Division of Network Medicine, the Department of Medicine, the Brigham and Women’s Hospital (Edwin K. Silverman, Division Chief) for providing morale supports and helping in announcements. This work was supported in part by grants from the US National Institute of Health (NIH) [R01 CA151993 (to SO), K07 CA190673 (to RN), R01 CA137178 (to ATC), K24 DK098311 (to ATC), and K07 CA172298 (to AIP)], and the friends of the Dana–Farber Cancer Institute (to SO). ATC is Damon Runyon Clinical Investigator. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. The funders (including Enzymatics, Inc.) did not have any role in planning the meeting, the decision to submit the manuscript for publication, or the writing of the manuscript. Use of Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC)-approved symbols for genes and gene products: We use symbols approved by HGNC and described at; those include BRAF, CD274, ERBB2, ESR1, FASN, KRAS, MLH1, PDCD1LG2, PGR, PIK3CA, and VHL. Gene names are italicized, while names of gene products are non-italicized. Non-official names are described in parenthesis where helpful.

Conflict of interest

The Second International MPE Meeting was sponsored in part by Enzymatics, Inc. ATC previously served as a consultant for Bayer Healthcare, Millennium Pharmaceuticals, Pozen Inc, and Pfizer Inc. The work was not funded by Enzymatics, Inc, Bayer Healthcare, Millennium Pharmaceuticals, Pozen Inc, or Pfizer Inc. All of the other authors declare no conflict of interest.


  1. 1.
    Ogino S, Fuchs CS, Giovannucci E (2012) How many molecular subtypes? Implications of the unique tumor principle in personalized medicine. Expert Rev Mol Diagn 12:621–628PubMedCentralPubMedGoogle Scholar
  2. 2.
    Ogino S, Lochhead P, Chan AT et al (2013) Molecular pathological epidemiology of epigenetics: Emerging integrative science to analyze environment, host, and disease. Mod Pathol 26:465–484PubMedCentralPubMedGoogle Scholar
  3. 3.
    Ogino S, Stampfer M (2010) Lifestyle factors and microsatellite instability in colorectal cancer: the evolving field of molecular pathological epidemiology. J Natl Cancer Inst 102:365–367PubMedCentralPubMedGoogle Scholar
  4. 4.
    Ogino S, Chan AT, Fuchs CS, Giovannucci E (2011) Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field. Gut 60:397–411PubMedCentralPubMedGoogle Scholar
  5. 5.
    Jacobs R, Voorneveld P, Kodach L, Hardwick J (2012) Cholesterol metabolism and colorectal cancers. Curr Opin Pharmacol 12:690–695PubMedGoogle Scholar
  6. 6.
    Curtin K, Slattery ML, Samowitz WS (2011) CpG island methylation in colorectal cancer: past, present and future. Pathol Res Int 2011:902674Google Scholar
  7. 7.
    Hughes LA, Simons CC, van den Brandt PA et al (2011) Body size, physical activity and risk of colorectal cancer with or without the CpG island methylator phenotype (CIMP). PLoS One 6:e18571PubMedCentralPubMedGoogle Scholar
  8. 8.
    Hughes LA, Khalid-de Bakker CA, Smits KM et al (2012) The CpG island methylator phenotype in colorectal cancer: progress and problems. Biochim Biophys Acta 1825:77–85PubMedGoogle Scholar
  9. 9.
    Iwagami S, Baba Y, Watanabe M et al (2012) Pyrosequencing assay to measure LINE-1 methylation level in esophageal squamous cell carcinoma. Ann Surg Oncol 19:2726–2732PubMedGoogle Scholar
  10. 10.
    Limburg PJ, Limsui D, Vierkant RA et al (2012) Postmenopausal hormone therapy and colorectal cancer risk in relation to somatic KRAS mutation status among older women. Cancer Epidemiol Biomarkers Prev 21:681–684PubMedCentralPubMedGoogle Scholar
  11. 11.
    Hughes LA, Williamson EJ, van Engeland M et al (2012) Body size and risk for colorectal cancers showing BRAF mutation or microsatellite instability: a pooled analysis. Int J Epidemiol 41:1060–1072PubMedGoogle Scholar
  12. 12.
    Ku CS, Cooper DN, Wu M et al (2012) Gene discovery in familial cancer syndromes by exome sequencing: prospects for the elucidation of familial colorectal cancer type X. Mod Pathol 25:1055–1068PubMedGoogle Scholar
  13. 13.
    Rex DK, Ahnen DJ, Baron JA et al (2012) Serrated lesions of the colorectum: review and recommendations from an expert panel. Am J Gastroenterol 107:1315–1329PubMedCentralPubMedGoogle Scholar
  14. 14.
    Koshiol J, Lin SW (2012) Can tissue-based immune markers be used for studying the natural history of cancer? Ann Epidemiol 22:520–530PubMedCentralPubMedGoogle Scholar
  15. 15.
    Fini L, Grizzi F, Laghi L (2012) Adaptive and innate immunity, non clonal players in colorectal cancer progression. In: Ettarh R (ed) Colorectal cancer biology—from genes to tumor: InTech, pp 323–340Google Scholar
  16. 16.
    Gay LJ, Mitrou PN, Keen J et al (2012) Dietary, lifestyle and clinico-pathological factors associated with APC mutations and promoter methylation in colorectal cancers from the EPIC-Norfolk Study. J Pathol 228:405–415PubMedGoogle Scholar
  17. 17.
    Galon J, Franck P, Marincola FM et al (2012) Cancer classification using the immunoscore: a worldwide task force. J Transl Med 10:205PubMedCentralPubMedGoogle Scholar
  18. 18.
    Chia WK, Ali R, Toh HC (2012) Aspirin as adjuvant therapy for colorectal cancer-reinterpreting paradigms. Nat Rev Clin Oncol 9:561–570PubMedGoogle Scholar
  19. 19.
    Dogan S, Shen R, Ang DC et al (2012) Molecular epidemiology of EGFR and KRAS mutations in 3026 lung adenocarcinomas: higher susceptibility of women to smoking-related KRAS-mutant cancers. Clin Cancer Res 18:6169–6177PubMedCentralPubMedGoogle Scholar
  20. 20.
    Spitz MR, Caporaso NE, Sellers TA (2012) Integrative cancer epidemiology—the next generation. Cancer Discov 2:1087–1090PubMedCentralPubMedGoogle Scholar
  21. 21.
    Shanmuganathan R, Nazeema Banu B, Amirthalingam L, Muthukumar H, Kaliaperumal R, Shanmugam K (2013) Conventional and nanotechniques for DNA methylation profiling. J Mol Diagn 15:17–26PubMedGoogle Scholar
  22. 22.
    Rosty C, Young JP, Walsh MD et al (2013) Colorectal carcinomas with KRAS mutation are associated with distinctive morphological and molecular features. Mod Pathol 26:825–834PubMedGoogle Scholar
  23. 23.
    Weijenberg MP, Hughes LA, Bours MJ, Simons CC, van Engeland M, van den Brandt PA (2013) The mTOR pathway and the role of energy balance throughout life in colorectal cancer etiology and prognosis: unravelling mechanisms through a multidimensional molecular epidemiologic approach. Curr Nutr Rep 2:19–26PubMedCentralPubMedGoogle Scholar
  24. 24.
    Buchanan DD, Win AK, Walsh MD et al (2013) Family history of colorectal cancer in BRAF p. V600E mutated colorectal cancer cases. Cancer Epidemiol Biomark Prev 22:917–926Google Scholar
  25. 25.
    Burnett-Hartman AN, Newcomb PA, Potter JD et al (2013) Genomic aberrations occurring in subsets of serrated colorectal lesions but not conventional adenomas. Cancer Res 73:2863–2872PubMedGoogle Scholar
  26. 26.
    Alvarez MC, Santos JC, Maniezzo N et al (2013) MGMT and MLH1 methylation in Helicobacter pylori-infected children and adults. World J Gastroenterol 19:3043–3051PubMedCentralPubMedGoogle Scholar
  27. 27.
    Hagland HR, Berg M, Jolma IW, Carlsen A, Soreide K (2013) Molecular pathways and cellular metabolism in colorectal cancer. Dig Surg 30:12–25PubMedGoogle Scholar
  28. 28.
    Zaidi N, Lupien L, Kuemmerle NB, Kinlaw WB, Swinnen JV, Smans K (2013) Lipogenesis and lipolysis: the pathways exploited by the cancer cells to acquire fatty acids. Prog Lipid Res 52:585–589PubMedCentralPubMedGoogle Scholar
  29. 29.
    Abbenhardt C, Poole EM, Kulmacz RJ et al (2013) Phospholipase A2G1B polymorphisms and risk of colorectal neoplasia. Int J Mol Epidemiol Genet 4:140–149PubMedCentralPubMedGoogle Scholar
  30. 30.
    Hughes LA, Melotte V, de Schrijver J et al (2013) The CpG island methylator phenotype: what’s in a name? Cancer Res 73:5858–5868PubMedGoogle Scholar
  31. 31.
    Bae JM, Kim JH, Cho NY, Kim TY, Kang GH (2013) Prognostic implication of the CpG island methylator phenotype in colorectal cancers depends on tumour location. Br J Cancer 109:1004–1012PubMedCentralPubMedGoogle Scholar
  32. 32.
    Amirian ES, Petrosino JF, Ajami NJ, Liu Y, Mims MP, Scheurer ME (2013) Potential role of gastrointestinal microbiota composition in prostate cancer risk. Infect Agents Cancer 8:42PubMedCentralPubMedGoogle Scholar
  33. 33.
    Hoffmeister M, Blaker H, Kloor M et al (2013) Body mass index and microsatellite instability in colorectal cancer: a population-based study. Cancer Epidemiol Biomark Prev 22:2303–2311Google Scholar
  34. 34.
    Araujo RF Jr, Lira GA, Guedes HG et al (2013) Lifestyle and family history influence cancer prognosis in Brazilian individuals. Pathol Res Pract 209:753–757PubMedGoogle Scholar
  35. 35.
    Esterhuyse MM, Kaufmann SH (2013) Diagnostic biomarkers are hidden in the infected host’s epigenome. Expert Rev Mol Diagn 13:625–637PubMedGoogle Scholar
  36. 36.
    Zhu Y, Yang SR, Wang PP et al (2014) Influence of pre-diagnostic cigarette smoking on colorectal cancer survival: overall and by tumour molecular phenotype. Br J Cancer 110:1359–1366PubMedCentralPubMedGoogle Scholar
  37. 37.
    Hagland HR, Soreide K (2015) Cellular metabolism in colorectal carcinogenesis: influence of lifestyle, gut microbiome and metabolic pathways. Cancer Lett 356:273–280PubMedGoogle Scholar
  38. 38.
    Shaheen NJ (2014) Editorial: what is behind the remarkable increase in esophageal adenocarcinoma? Am J Gastroenterol 109:345–347PubMedGoogle Scholar
  39. 39.
    Brandstedt J, Wangefjord S, Nodin B, Eberhard J, Jirstrom K, Manjer J (2014) Associations of hormone replacement therapy and oral contraceptives with risk of colorectal cancer defined by clinicopathological factors, beta-catenin alterations, expression of cyclin D1, p53, and microsatellite-instability. BMC Cancer 14:371PubMedCentralPubMedGoogle Scholar
  40. 40.
    Coppede F (2014) The role of epigenetics in colorectal cancer. Expert Rev Gastroenterol Hepatol 8:935–948Google Scholar
  41. 41.
    Bishehsari F, Mahdavinia M, Vacca M, Malekzadeh R, Mariani-Costantini R (2014) Epidemiological transition of colorectal cancer in developing countries: environmental factors, molecular pathways, and opportunities for prevention. World J Gastroenterol 20:6055–6072PubMedCentralPubMedGoogle Scholar
  42. 42.
    Cross AJ, Moore SC, Boca S et al (2014) A prospective study of serum metabolites and colorectal cancer risk. Cancer 120:3049–3057PubMedGoogle Scholar
  43. 43.
    Simons CC, van den Brandt PA, Stehouwer C, van Engeland M, Weijenberg MP (2014) Body size, physical activity, early life energy restriction, and associations with methylated insulin-like growth factor binding protein genes in colorectal cancer. Cancer Epidemiol Biomark Prev 23:1852–1862Google Scholar
  44. 44.
    Haque TR, Bradshaw PT, Crockett SD (2014) Risk factors for serrated polyps of the colorectum. Dig Dis Sci 59:2874–2889PubMedGoogle Scholar
  45. 45.
    Ryan BM, Wolff RK, Valeri N et al (2014) An analysis of genetic factors related to risk of inflammatory bowel disease and colon cancer. Cancer Epidemiol 38:583–590PubMedGoogle Scholar
  46. 46.
    Li P, Wu H, Zhang H et al (2015) Aspirin use after diagnosis but not prediagnosis improves established colorectal cancer survival: a meta-analysis. Gut. doi: 10.1136/gutjnl-2014-308260
  47. 47.
    Huser V, Sincan M, Cimino JJ (2014) Developing genomic knowledge bases and databases to support clinical management: current perspectives. Pharmacogen Personal Med 7:275–283Google Scholar
  48. 48.
    Wennersten C, Andersson G, Boman K, Nodin B, Gaber A, Jirstrom K (2014) Incident urothelial cancer in the Malmo Diet and Cancer Study: cohort characteristics and further validation of ezrin as a prognostic biomarker. Diagn Pathol 9:189PubMedCentralPubMedGoogle Scholar
  49. 49.
    Mikeska T, Craig JM (2014) DNA methylation biomarkers: cancer and beyond. Genes 5:821–864PubMedCentralPubMedGoogle Scholar
  50. 50.
    Campbell PT, Deka A, Briggs P et al (2014) Establishment of the cancer prevention study II nutrition cohort colorectal tissue repository. Cancer Epidemiol Biomark Prev 23:2694–2702Google Scholar
  51. 51.
    Wild CP, Bucher JR, de Jong BW et al (2015) Translational cancer research: balancing prevention and treatment to combat cancer globally. J Natl Cancer Inst 107:353PubMedGoogle Scholar
  52. 52.
    Caiazza F, Ryan EJ, Doherty G, Winter DC, Sheahan K (2015) Estrogen receptors and their implications in colorectal carcinogenesis. Front Oncol 5:Article 19Google Scholar
  53. 53.
    Ng JM, Yu J (2015) Promoter hypermethylation of tumour suppressor genes as potential biomarkers in colorectal cancer. Int J Mol Sci 16:2472–2496PubMedCentralPubMedGoogle Scholar
  54. 54.
    Tillmans LS, Vierkant RA, Wang AH et al (2015) Associations between environmental exposures and incident colorectal cancer by ESR2 protein expression level in a population-based cohort of older women. Cancer Epidemiol Biomark Prev 24:713–719Google Scholar
  55. 55.
    Witvliet MI (2014) World health survey: a useful yet underutilized global health data source. Austin J Public Health Epidemiol 1:id1012Google Scholar
  56. 56.
    Potter S (2014) Body mass index 112 Success Secrets—112 Most Asked Questions On Body mass index—What You Need To Know. Kindle edition ed: Emeroe PublishingGoogle Scholar
  57. 57.
    Cisyk AL, Penner-Goeke S, Lichtensztejn Z et al (2015) Characterizing the prevalence of chromosome instability in interval colorectal cancer. Neoplasia 17:306–316PubMedCentralPubMedGoogle Scholar
  58. 58.
    Weisenberger DJ, Levine AJ, Long TI et al (2015) Association of the colorectal CpG island methylator phenotype with molecular features, risk factors and family history. Cancer Epidemiol Biomark Prev 24:512–519Google Scholar
  59. 59.
    Gao C (2015) Molecular pathological epidemiology: an interdisciplinary field for study of hepatocellular carcinoma. Austin J Gastroenterol 2:1040Google Scholar
  60. 60.
    Szylberg L, Janiczek M, Popiel A, Marszalek A (2015) Serrated polyps and their alternative pathway to the colorectal cancer: a systematic review. Gastroenterol Res Pract 2015:ID 573814Google Scholar
  61. 61.
    Sherman ME, Howatt W, Blows FM, Pharoah P, Hewitt SM, Garcia-Closas M (2010) Molecular pathology in epidemiologic studies: a primer on key considerations. Cancer Epidemiol Biomark Prev 19:966–972Google Scholar
  62. 62.
    Gaudet MM, Sherman ME, Thun MJ (2012) Learning from disease heterogeneity. Lancet Oncol 13:862–863PubMedGoogle Scholar
  63. 63.
    Begg CB, Zabor EC (2012) Detecting and exploiting etiologic heterogeneity in epidemiologic studies. Am J Epidemiol 176:512–518PubMedCentralPubMedGoogle Scholar
  64. 64.
    Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) A conceptual and methodological framework for investigating etiologic heterogeneity. Stat Med 32:5039–5052PubMedCentralPubMedGoogle Scholar
  65. 65.
    Field AE, Camargo CA, Ogino S (2013) The merits of subtyping obestity: one size does not fit all. JAMA 310:2147–2148PubMedGoogle Scholar
  66. 66.
    Yamauchi M, Morikawa T, Kuchiba A et al (2012) Assessment of colorectal cancer molecular features along bowel subsites challenges the conception of distinct dichotomy of proximal versus distal colorectum. Gut 61:847–854PubMedCentralPubMedGoogle Scholar
  67. 67.
    Yamauchi M, Lochhead P, Morikawa T et al (2012) Colorectal cancer: a tale of two sides or a continuum? Gut 61:794–797PubMedCentralPubMedGoogle Scholar
  68. 68.
    Lochhead P, Chan AT, Nishihara R et al (2015) Etiologic field effect: reappraisal of the field effect concept in cancer predisposition and progression. Mod Pathol 28:14–29PubMedGoogle Scholar
  69. 69.
    Nishi A, Kawachi I, Koenen KC, Wu K, Nishihara R, Ogino S (2015) Lifecourse epidemiology and molecular pathological epidemiology. Am J Prev Med 48:116–119PubMedGoogle Scholar
  70. 70.
    Ogino S, Giovannucci E (2012) Commentary: lifestyle factors and colorectal cancer microsatellite instability—molecular pathological epidemiology science, based on unique tumour principle. In J Epidemiol 41:1072–1074Google Scholar
  71. 71.
    Ogino S, King EE, Beck AH, Sherman ME, Milner DA, Giovannucci E (2012) Interdisciplinary education to integrate pathology and epidemiology: towards molecular and population-level health science. Am J Epidemiol 176:659–667PubMedCentralPubMedGoogle Scholar
  72. 72.
    DerSimonian R, Charette LJ, McPeek B, Mosteller F (1982) Reporting on methods in clinical trials. N Engl J Med 306:1332–1337PubMedGoogle Scholar
  73. 73.
    Schulz KF, Altman DG, Moher D, Group C (2010) CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. PLoS Med 7:e1000251Google Scholar
  74. 74.
    Samowitz WS, Albertsen H, Sweeney C et al (2006) Association of smoking, CpG island methylator phenotype, and V600E BRAF mutations in colon cancer. J Natl Cancer Inst 98:1731–1738PubMedGoogle Scholar
  75. 75.
    Limsui D, Vierkant RA, Tillmans LS et al (2010) Cigarette smoking and colorectal cancer risk by molecularly defined subtypes. J Natl Cancer Inst 102:1012–1022PubMedCentralPubMedGoogle Scholar
  76. 76.
    Nishihara R, Morikawa T, Kuchiba A et al (2013) A prospective study of duration of smoking cessation and colorectal cancer risk by epigenetics-related tumor classification. Am J Epidemiol 178:84–100PubMedCentralPubMedGoogle Scholar
  77. 77.
    Curtin K, Samowitz WS, Wolff RK, Herrick J, Caan BJ, Slattery ML (2009) Somatic alterations, metabolizing genes and smoking in rectal cancer. Int J Cancer 125:158–164PubMedCentralPubMedGoogle Scholar
  78. 78.
    Poynter JN, Haile RW, Siegmund KD et al (2009) Associations between smoking, alcohol consumption, and colorectal cancer, overall and by tumor microsatellite instability status. Cancer Epidemiol Biomark Prev 18:2745–2750Google Scholar
  79. 79.
    Lindor NM, Yang P, Evans I et al (2010) Alpha-1-antitrypsin deficiency and smoking as risk factors for mismatch repair deficient colorectal cancer: a study from the colon cancer family registry. Mol Genet Metab 99:157–159PubMedCentralPubMedGoogle Scholar
  80. 80.
    Chia VM, Newcomb PA, Bigler J, Morimoto LM, Thibodeau SN, Potter JD (2006) Risk of microsatellite-unstable colorectal cancer is associated jointly with smoking and nonsteroidal anti-inflammatory drug use. Cancer Res 66:6877–6883PubMedGoogle Scholar
  81. 81.
    Barrow TM, Michels KB (2014) Epigenetic epidemiology of cancer. Biochem Biophys Res Commun 455:70–83PubMedGoogle Scholar
  82. 82.
    Slattery ML, Curtin K, Anderson K et al (2000) Associations between cigarette smoking, lifestyle factors, and microsatellite instability in colon tumors. J Natl Cancer Inst 92:1831–1836PubMedGoogle Scholar
  83. 83.
    Campbell PT, Jacobs ET, Ulrich CM et al (2010) Case-control study of overweight, obesity, and colorectal cancer risk, overall and by tumor microsatellite instability status. J Natl Cancer Inst 102:391–400PubMedCentralPubMedGoogle Scholar
  84. 84.
    Satia JA, Keku T, Galanko JA et al (2005) Diet, lifestyle, and genomic instability in the north Carolina colon cancer study. Cancer Epidemiol Biomark Prev 14:429–436Google Scholar
  85. 85.
    Arain MA, Sawhney M, Sheikh S et al (2010) CIMP status of interval colon cancers: another piece to the puzzle. Am J Gastroenterol 105:1189–1195PubMedGoogle Scholar
  86. 86.
    Nishihara R, Wu K, Lochhead P et al (2013) Long-term colorectal cancer incidence and mortality after lower endoscopy. N Engl J Med 369:1095–1105PubMedGoogle Scholar
  87. 87.
    Ogino S, Lochhead P, Giovannucci E, Meyerhardt JA, Fuchs CS, Chan AT (2014) Discovery of colorectal cancer PIK3CA mutation as potential predictive biomarker: power and promise of molecular pathological epidemiology. Oncogene 33:2949–2955PubMedCentralPubMedGoogle Scholar
  88. 88.
    Jung S, Spiegelman D, Baglietto L et al (2013) Fruit and vegetable intake and risk of breast cancer by hormone receptor status. J Natl Cancer Inst 105:219–236PubMedCentralPubMedGoogle Scholar
  89. 89.
    Lao VV, Grady WM (2011) Epigenetics and colorectal cancer. Nat Rev Gastroenterol Hepatol 8:686–700PubMedCentralPubMedGoogle Scholar
  90. 90.
    Colussi D, Brandi G, Bazzoli F, Ricciardiello L (2013) Molecular pathways involved in colorectal cancer: implications for disease behavior and prevention. Int J Mol Sci 14:16365–16385PubMedCentralPubMedGoogle Scholar
  91. 91.
    Bardhan K, Liu K (2013) Epigenetics and colorectal cancer pathogenesis. Cancers 5:676–713Google Scholar
  92. 92.
    Zoratto F, Rossi L, Verrico M et al (2014) Focus on genetic and epigenetic events of colorectal cancer pathogenesis: implications for molecular diagnosis. Tumour Biol 35:6195–6206PubMedGoogle Scholar
  93. 93.
    Aleman JO, Eusebi LH, Ricciardiello L, Patidar K, Sanyal AJ, Holt PR (2014) Mechanisms of obesity-induced gastrointestinal neoplasia. Gastroenterology 146:357–373PubMedCentralPubMedGoogle Scholar
  94. 94.
    Lin JH, Giovannucci E (2014) Environmental exposure and tumor heterogeneity in colorectal cancer risk and outcomes. Curr Colorectal Cancer Rep 10:94–104Google Scholar
  95. 95.
    Song M, Garrett WS, Chan AT (2015) Nutrients, foods, and colorectal cancer prevention. Gastroenterology 148:1244–1260.e16Google Scholar
  96. 96.
    Jeon JY, Meyerhardt JA (2012) Energy in and energy out: what matters for survivors of colorectal cancer? J Clin Oncol 30:7–10PubMedGoogle Scholar
  97. 97.
    Campbell PT, Patel AV, Newton CC, Jacobs EJ, Gapstur SM (2013) Associations of recreational physical activity and leisure time spent sitting with colorectal cancer survival. J Clin Oncol 31:876–885PubMedGoogle Scholar
  98. 98.
    Bathe OF, Farshidfar F (2014) From genotype to functional phenotype: unraveling the metabolomic features of colorectal cancer. Genes 5:536–560PubMedCentralPubMedGoogle Scholar
  99. 99.
    Kuchiba A, Morikawa T, Yamauchi M et al (2012) Body mass index and risk of colorectal cancer according to fatty acid synthase expression in the nurses’ health study. J Natl Cancer Inst 104:415–420PubMedCentralPubMedGoogle Scholar
  100. 100.
    Ogino S, kawasaki T, Ogawa A, Kirkner GJ, Loda M, Fuchs CS (2007) Fatty acid synthase overexpression in colorectal cancer is associated with microsatellite instability, independent of CpG island methylator phenotype. Hum Pathol 38:842–849PubMedGoogle Scholar
  101. 101.
    Herbert K, Kerr R, Kerr DJ, Church DN (2014) Are NSAIDs coming back to colorectal cancer therapy or not? Curr Colorectal Cancer Rep 10:363–371Google Scholar
  102. 102.
    Tougeron D, Sha D, Manthravadi S, Sinicrope FA (2014) Aspirin and colorectal cancer: back to the Future. Clin Cancer Res 20:1087–1094PubMedCentralPubMedGoogle Scholar
  103. 103.
    Chan AT, Ogino S, Fuchs CS (2007) Aspirin and the risk of colorectal cancer in relation to the expression of COX-2. N Engl J Med 356:2131–2142PubMedGoogle Scholar
  104. 104.
    Chan AT, Ogino S, Fuchs CS (2009) Aspirin use and survival after diagnosis of colorectal cancer. JAMA 302:649–658PubMedCentralPubMedGoogle Scholar
  105. 105.
    Chan AT, Ogino S, Giovannucci EL, Fuchs CS (2011) Inflammatory markers are associated with risk of colorectal cancer and chemopreventive response to anti-inflammatory drugs. Gastroenterology 140:799–808, quiz e711Google Scholar
  106. 106.
    Liao X, Lochhead P, Nishihara R et al (2012) Aspirin use, tumor PIK3CA mutation status, and colorectal cancer survival. N Engl J Med 367:1596–1606PubMedCentralPubMedGoogle Scholar
  107. 107.
    Nishihara R, Lochhead P, Kuchiba A et al (2013) Aspirin use and risk of colorectal cancer according to BRAF mutation status. JAMA 309:2563–2571PubMedCentralPubMedGoogle Scholar
  108. 108.
    Nan H, Morikawa T, Suuriniemi M et al (2013) Aspirin use, 8q24 single nucleotide polymorphism rs6983267, and colorectal cancer according to CTNNB1 alterations. J Natl Cancer Inst 105:1852–1861PubMedCentralPubMedGoogle Scholar
  109. 109.
    Fink SP, Yamauchi M, Nishihara R et al (2014) Aspirin and the risk of colorectal cancer in relation to the expression of 15-hydroxyprostaglandin dehydrogenase (HPGD). Sci Transl Med 6:233re232Google Scholar
  110. 110.
    Domingo E, Church DN, Sieber O et al (2013) Evaluation of PIK3CA mutation as a predictor of benefit from NSAID therapy in colorectal cancer. J Clin Oncol 31:4297–4305PubMedGoogle Scholar
  111. 111.
    Garcia-Closas M, Couch FJ, Lindstrom S et al (2013) Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet 45:392–398, 398e391–398e392Google Scholar
  112. 112.
    Tamimi RM, Colditz GA, Hazra A et al (2012) Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer. Breast Cancer Res Treat 131:159–167PubMedCentralPubMedGoogle Scholar
  113. 113.
    Millikan RC, Newman B, Tse CK et al (2008) Epidemiology of basal-like breast cancer. Breast Cancer Res Treat 109:123–139PubMedCentralPubMedGoogle Scholar
  114. 114.
    Phipps AI, Buist DS, Malone KE et al (2011) Family history of breast cancer in first-degree relatives and triple-negative breast cancer risk. Breast Cancer Res Treat 126:671–678PubMedCentralPubMedGoogle Scholar
  115. 115.
    Yang XR, Sherman ME, Rimm DL et al (2007) Differences in risk factors for breast cancer molecular subtypes in a population-based study. Cancer Epidemiol Biomarkers Prev 16:439–443PubMedGoogle Scholar
  116. 116.
    Kostic AD, Ojesina AI, Pedamallu CS et al (2011) PathSeq: software to identify or discover microbes by deep sequencing of human tissue. Nat Biotechnol 29:393–396PubMedCentralPubMedGoogle Scholar
  117. 117.
    Kostic AD, Gevers D, Pedamallu CS et al (2012) Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res 22:292–298PubMedCentralPubMedGoogle Scholar
  118. 118.
    Tahara T, Yamamoto E, Suzuki H et al (2014) Fusobacterium in colonic flora and molecular features of colorectal carcinoma. Cancer ResGoogle Scholar
  119. 119.
    Mima K, Sukawa Y, Nishihara R et al (2015) Fusobacterium nucleatum and T-cells in colorectal carcinoma. JAMA Oncol (in press)Google Scholar
  120. 120.
    Kostic AD, Chun E, Robertson L et al (2013) Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14:207–215PubMedCentralPubMedGoogle Scholar
  121. 121.
    Cancer Genome AtlasResearch N (2014) Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513:202–209Google Scholar
  122. 122.
    Morton LM, Sampson JN, Cerhan JR et al (2014) Rationale and design of the International Lymphoma Epidemiology Consortium (InterLymph) non-Hodgkin lymphoma subtypes project. J Natl Cancer Inst Monogr 2014:1–14Google Scholar
  123. 123.
    Begg CB (2011) A strategy for distinguishing optimal cancer subtypes. Int J Cancer 129:931–937PubMedCentralPubMedGoogle Scholar
  124. 124.
    Wang M, Kuchiba A, Ogino S (2015) A meta-regression method for studying etiologic heterogeneity across disease subtypes classified by multiple biomarkers. Am J Epidemiol (in press)Google Scholar
  125. 125.
    Chatterjee N, Sinha S, Diver WR, Feigelson HS (2010) Analysis of cohort studies with multivariate and partially observed disease classification data. Biometrika 97:683–698PubMedCentralPubMedGoogle Scholar
  126. 126.
    Chatterjee N (2004) A two-stage regression model for epidemiological studies with multivariate disease classification data. J Am Stat Assoc 99:127–138Google Scholar
  127. 127.
    Rosner B, Glynn RJ, Tamimi RM et al (2013) Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers. Am J Epidemiol 178:296–308PubMedCentralPubMedGoogle Scholar
  128. 128.
    Leek JT, Scharpf RB, Bravo HC et al (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11:733–739PubMedGoogle Scholar
  129. 129.
    Jaffe AE, Irizarry RA (2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol 15:R31PubMedCentralPubMedGoogle Scholar
  130. 130.
    Houseman EA, Kim S, Kelsey KT, Wiencke JK (2015) DNA methylation in whole blood: uses and challenges. Curr Envir Health Rep (in press)Google Scholar
  131. 131.
    Beck AH, Knoblauch NW, Hefti MM et al (2013) Significance analysis of prognostic signatures. PLoS Comput Biol 9:e1002875PubMedCentralPubMedGoogle Scholar
  132. 132.
    Sherman ME, Figueroa JD, Henry JE, Clare SE, Rufenbarger C, Storniolo AM (2012) The Susan G. Komen for the Cure Tissue Bank at the IU Simon Cancer Center: a unique resource for defining the “molecular histology” of the breast. Cancer Prev Res (Phila) 5:528–535Google Scholar
  133. 133.
    Figueroa JD, Pfeiffer RM, Patel DA et al (2014) Terminal duct lobular unit involution of the normal breast: implications for breast cancer etiology. J Natl Cancer Inst 106:dju286Google Scholar
  134. 134.
    Faupel-Badger JM, Arcaro KF, Balkam JJ et al (2013) Postpartum remodeling, lactation, and breast cancer risk: summary of a National Cancer Institute-sponsored workshop. J Natl Cancer Inst 105:166–174PubMedCentralPubMedGoogle Scholar
  135. 135.
    Ghosh K, Vachon CM, Pankratz VS et al (2010) Independent association of lobular involution and mammographic breast density with breast cancer risk. J Natl Cancer Inst 102:1716–1723PubMedCentralPubMedGoogle Scholar
  136. 136.
    Ghosh K, Hartmann LC, Reynolds C et al (2010) Association between mammographic density and age-related lobular involution of the breast. J Clin Oncol 28:2207–2212PubMedCentralPubMedGoogle Scholar
  137. 137.
    Lochhead P, Chan AT, Giovannucci E et al (2014) Progress and opportunities in molecular pathological epidemiology of colorectal premalignant lesions. Am J Gastroenterol 109:1205–1214PubMedCentralPubMedGoogle Scholar
  138. 138.
    Roman-Perez E, Casbas-Hernandez P, Pirone JR et al (2012) Gene expression in extratumoral microenvironment predicts clinical outcome in breast cancer patients. Breast Cancer Res 14:R51PubMedCentralPubMedGoogle Scholar
  139. 139.
    Sun X, Sandhu R, Figueroa JD, Gierach GL, Sherman ME, Troester MA (2014) Benign breast tissue composition in breast cancer patients: association with risk factors, clinical variables, and gene expression. Cancer Epidemiol Biomarkers Prev 23:2810–2818PubMedGoogle Scholar
  140. 140.
    Palmer JR, Viscidi E, Troester MA et al (2014) Parity, lactation, and breast cancer subtypes in African American women: results from the AMBER Consortium. J Natl Cancer Inst 106:dju237Google Scholar
  141. 141.
    Hakimi AA, Furberg H, Zabor EC et al (2013) An epidemiologic and genomic investigation into the obesity paradox in renal cell carcinoma. J Natl Cancer Inst 105:1862–1870PubMedCentralPubMedGoogle Scholar
  142. 142.
    Markt SC, Valdimarsdottir UA, Shui IM et al (2015) Circadian clock genes and risk of fatal prostate cancer. Cancer Causes Control 26:25–33PubMedCentralPubMedGoogle Scholar
  143. 143.
    Pettersson A, Lis RT, Meisner A et al (2013) Modification of the association between obesity and lethal prostate cancer by TMPRSS2:ERG. J Natl Cancer Inst 105:1881–1890PubMedCentralPubMedGoogle Scholar
  144. 144.
    Gates MA, Rosner BA, Hecht JL, Tworoger SS (2010) Risk factors for epithelial ovarian cancer by histologic subtype. Am J Epidemiol 171:45–53PubMedCentralPubMedGoogle Scholar
  145. 145.
    Poole EM, Merritt MA, Jordan SJ et al (2013) Hormonal and reproductive risk factors for epithelial ovarian cancer by tumor aggressiveness. Cancer Epidemiol Biomark Prev 22:429–437Google Scholar
  146. 146.
    von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP (2007) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med 4:e296Google Scholar
  147. 147.
    Vandenbroucke JP, von Elm E, Altman DG et al (2007) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med 4:e297PubMedCentralPubMedGoogle Scholar
  148. 148.
    Terry MB, Knight JA (2011) STROBE-ME - Illuminating methodological issues for the reporting of molecular epidemiology data. Prev Med 53:388–389PubMedGoogle Scholar
  149. 149.
    Gallo V, Egger M, McCormack V et al (2011) STrengthening the Reporting of OBservational studies in Epidemiology—Molecular Epidemiology (STROBE-ME): an extension of the STROBE statement. PLoS Med 8:e1001117PubMedCentralPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shuji Ogino
    • 1
    • 2
    • 3
    Email author
  • Peter T. Campbell
    • 4
  • Reiko Nishihara
    • 2
    • 3
    • 5
    • 6
  • Amanda I. Phipps
    • 7
    • 8
  • Andrew H. Beck
    • 9
    • 10
  • Mark E. Sherman
    • 11
    • 12
  • Andrew T. Chan
    • 13
    • 14
  • Melissa A. Troester
    • 15
  • Adam J. Bass
    • 2
    • 10
  • Kathryn C. Fitzgerald
    • 3
    • 6
  • Rafael A. Irizarry
    • 5
    • 16
  • Karl T. Kelsey
    • 17
  • Hongmei Nan
    • 18
  • Ulrike Peters
    • 7
    • 8
  • Elizabeth M. Poole
    • 14
  • Zhi Rong Qian
    • 2
  • Rulla M. Tamimi
    • 3
    • 14
  • Eric J. Tchetgen Tchetgen
    • 3
    • 5
  • Shelley S. Tworoger
    • 3
    • 14
  • Xuehong Zhang
    • 14
  • Edward L. Giovannucci
    • 3
    • 6
    • 14
  • Piet A. van den Brandt
    • 19
  • Bernard A. Rosner
    • 5
    • 14
  • Molin Wang
    • 3
    • 5
    • 14
  • Nilanjan Chatterjee
    • 12
  • Colin B. Begg
    • 20
  1. 1.Department of Pathology, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA
  2. 2.Department of Medical Oncology, Dana-Farber Cancer InstituteHarvard Medical SchoolBostonUSA
  3. 3.Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonUSA
  4. 4.Epidemiology Research ProgramAmerican Cancer SocietyAtlantaUSA
  5. 5.Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonUSA
  6. 6.Department of NutritionHarvard T.H. Chan School of Public HealthBostonUSA
  7. 7.Department of EpidemiologyUniversity of WashingtonSeattleUSA
  8. 8.Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUSA
  9. 9.Department of Pathology, Beth Israel Deaconess Medical CenterHarvard Medical SchoolBostonUSA
  10. 10.The Broad InstituteCambridgeUSA
  11. 11.Breast and Gynecologic Cancer Research Group, Division of Cancer PreventionNational Cancer InstituteBethesdaUSA
  12. 12.Division of Cancer Epidemiology and GeneticsNational Cancer InstituteRockvilleUSA
  13. 13.Division of GastroenterologyMassachusetts General HospitalBostonUSA
  14. 14.Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA
  15. 15.Department of EpidemiologyUniversity of North Carolina at Chapel HillChapel HillUSA
  16. 16.Department of Biostatics and Computational Biology, Dana-Farber Cancer InstituteHarvard Medical SchoolBostonUSA
  17. 17.Department of Pathology and Laboratory MedicineBrown UniversityProvidenceUSA
  18. 18.Department of Epidemiology, Richard M. Fairbanks School of Public Health, Melvin and Bren Simon Cancer CenterIndiana UniversityIndianapolisUSA
  19. 19.Department of EpidemiologyMaastricht UniversityMaastrichtThe Netherlands
  20. 20.Department of Epidemiology and BiostatisticsMemorial Sloan Kettering Cancer CenterNew YorkUSA

Personalised recommendations